Overview

Dataset statistics

Number of variables18
Number of observations4721
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory829.8 KiB
Average record size in memory180.0 B

Variable types

Categorical3
Numeric15

Alerts

numbervmailmessages is highly overall correlated with voicemailplanHigh correlation
totaldayminutes is highly overall correlated with totaldaychargeHigh correlation
totaldaycharge is highly overall correlated with totaldayminutesHigh correlation
totaleveminutes is highly overall correlated with totalevechargeHigh correlation
totalevecharge is highly overall correlated with totaleveminutesHigh correlation
totalnightminutes is highly overall correlated with totalnightchargeHigh correlation
totalnightcharge is highly overall correlated with totalnightminutesHigh correlation
totalintlminutes is highly overall correlated with totalintlchargeHigh correlation
totalintlcharge is highly overall correlated with totalintlminutesHigh correlation
voicemailplan is highly overall correlated with numbervmailmessagesHigh correlation
internationalplan is highly imbalanced (54.7%)Imbalance
numbervmailmessages has 3483 (73.8%) zerosZeros
numbercustomerservicecalls has 971 (20.6%) zerosZeros

Reproduction

Analysis started2023-03-22 09:49:21.971600
Analysis finished2023-03-22 09:50:03.077384
Duration41.11 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
0
4079 
1
642 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4721
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4079
86.4%
1 642
 
13.6%

Length

2023-03-22T15:20:03.182490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:20:03.328494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4079
86.4%
1 642
 
13.6%

Most occurring characters

ValueCountFrequency (%)
0 4079
86.4%
1 642
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4721
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4079
86.4%
1 642
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4721
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4079
86.4%
1 642
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4079
86.4%
1 642
 
13.6%

accountlength
Real number (ℝ)

Distinct210
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.918238
Minimum1
Maximum217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:03.495495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q173
median100
Q3127
95-th percentile166
Maximum217
Range216
Interquartile range (IQR)54

Descriptive statistics

Standard deviation39.299039
Coefficient of variation (CV)0.39331198
Kurtosis-0.22666394
Mean99.918238
Median Absolute Deviation (MAD)27
Skewness0.058607176
Sum471714
Variance1544.4145
MonotonicityNot monotonic
2023-03-22T15:20:03.661487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 64
 
1.4%
87 56
 
1.2%
93 56
 
1.2%
105 55
 
1.2%
100 53
 
1.1%
112 53
 
1.1%
95 52
 
1.1%
116 50
 
1.1%
101 50
 
1.1%
78 49
 
1.0%
Other values (200) 4183
88.6%
ValueCountFrequency (%)
1 11
0.2%
2 2
 
< 0.1%
3 8
0.2%
4 3
 
0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 4
 
0.1%
8 1
 
< 0.1%
9 3
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
217 3
0.1%
216 1
 
< 0.1%
215 1
 
< 0.1%
212 2
< 0.1%
210 2
< 0.1%
209 3
0.1%
208 1
 
< 0.1%
205 2
< 0.1%
204 1
 
< 0.1%
202 2
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
0
4272 
1
449 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4721
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 4272
90.5%
1 449
 
9.5%

Length

2023-03-22T15:20:03.787488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:20:03.891945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4272
90.5%
1 449
 
9.5%

Most occurring characters

ValueCountFrequency (%)
0 4272
90.5%
1 449
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4721
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4272
90.5%
1 449
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
Common 4721
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4272
90.5%
1 449
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4272
90.5%
1 449
 
9.5%

voicemailplan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.8 KiB
0
3482 
1
1239 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4721
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3482
73.8%
1 1239
 
26.2%

Length

2023-03-22T15:20:03.985946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-22T15:20:04.093896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3482
73.8%
1 1239
 
26.2%

Most occurring characters

ValueCountFrequency (%)
0 3482
73.8%
1 1239
 
26.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4721
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3482
73.8%
1 1239
 
26.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4721
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3482
73.8%
1 1239
 
26.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3482
73.8%
1 1239
 
26.2%

numbervmailmessages
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6454141
Minimum0
Maximum48
Zeros3483
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:04.204758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316
95-th percentile36
Maximum48
Range48
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.420311
Coefficient of variation (CV)1.7553413
Kurtosis0.20184797
Mean7.6454141
Median Absolute Deviation (MAD)0
Skewness1.3573374
Sum36094
Variance180.10475
MonotonicityNot monotonic
2023-03-22T15:20:04.366765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 3483
73.8%
31 77
 
1.6%
28 63
 
1.3%
29 63
 
1.3%
33 62
 
1.3%
24 60
 
1.3%
27 60
 
1.3%
26 56
 
1.2%
30 56
 
1.2%
32 51
 
1.1%
Other values (34) 690
 
14.6%
ValueCountFrequency (%)
0 3483
73.8%
4 1
 
< 0.1%
6 2
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
10 4
 
0.1%
11 2
 
< 0.1%
12 11
 
0.2%
13 4
 
0.1%
14 9
 
0.2%
ValueCountFrequency (%)
48 4
 
0.1%
47 4
 
0.1%
46 8
 
0.2%
45 10
 
0.2%
44 9
 
0.2%
43 16
 
0.3%
42 16
 
0.3%
41 21
0.4%
40 26
0.6%
39 40
0.8%

totaldayminutes
Real number (ℝ)

Distinct1912
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.70483
Minimum25.9
Maximum338.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:04.495769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum25.9
5-th percentile93.2
Q1144
median180.1
Q3216.7
95-th percentile271.2
Maximum338.4
Range312.5
Interquartile range (IQR)72.7

Descriptive statistics

Standard deviation53.154035
Coefficient of variation (CV)0.29414839
Kurtosis-0.1592517
Mean180.70483
Median Absolute Deviation (MAD)36.3
Skewness0.021507088
Sum853107.5
Variance2825.3515
MonotonicityNot monotonic
2023-03-22T15:20:04.632771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174.5 9
 
0.2%
180 9
 
0.2%
177.1 9
 
0.2%
189.3 9
 
0.2%
159.5 9
 
0.2%
154 9
 
0.2%
143.7 8
 
0.2%
185 8
 
0.2%
168.4 8
 
0.2%
184.5 8
 
0.2%
Other values (1902) 4635
98.2%
ValueCountFrequency (%)
25.9 1
< 0.1%
27 1
< 0.1%
29.9 1
< 0.1%
30.9 1
< 0.1%
34 1
< 0.1%
34.5 1
< 0.1%
35.1 1
< 0.1%
37.4 1
< 0.1%
37.7 1
< 0.1%
37.8 1
< 0.1%
ValueCountFrequency (%)
338.4 1
< 0.1%
337.4 1
< 0.1%
335.5 1
< 0.1%
334.3 1
< 0.1%
332.9 1
< 0.1%
332.1 1
< 0.1%
329.8 1
< 0.1%
328.1 1
< 0.1%
326.5 1
< 0.1%
326.3 1
< 0.1%

totaldaycalls
Real number (ℝ)

Distinct113
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.08028
Minimum42
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:04.808134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile67
Q187
median100
Q3113
95-th percentile133
Maximum158
Range116
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.528058
Coefficient of variation (CV)0.19512393
Kurtosis-0.1463992
Mean100.08028
Median Absolute Deviation (MAD)13
Skewness-0.012371531
Sum472479
Variance381.34504
MonotonicityNot monotonic
2023-03-22T15:20:04.948964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 111
 
2.4%
102 107
 
2.3%
95 99
 
2.1%
100 98
 
2.1%
97 98
 
2.1%
94 98
 
2.1%
112 97
 
2.1%
110 96
 
2.0%
108 95
 
2.0%
96 94
 
2.0%
Other values (103) 3728
79.0%
ValueCountFrequency (%)
42 2
 
< 0.1%
44 4
0.1%
45 3
0.1%
46 1
 
< 0.1%
47 3
0.1%
48 4
0.1%
49 3
0.1%
50 1
 
< 0.1%
51 4
0.1%
52 6
0.1%
ValueCountFrequency (%)
158 3
 
0.1%
157 2
 
< 0.1%
156 3
 
0.1%
152 2
 
< 0.1%
151 6
0.1%
150 6
0.1%
149 1
 
< 0.1%
148 6
0.1%
147 10
0.2%
146 7
0.1%

totaldaycharge
Real number (ℝ)

Distinct1912
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.720381
Minimum4.4
Maximum57.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:05.338599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.4
5-th percentile15.84
Q124.48
median30.62
Q336.84
95-th percentile46.1
Maximum57.53
Range53.13
Interquartile range (IQR)12.36

Descriptive statistics

Standard deviation9.036148
Coefficient of variation (CV)0.29414179
Kurtosis-0.15916187
Mean30.720381
Median Absolute Deviation (MAD)6.17
Skewness0.021522326
Sum145030.92
Variance81.65197
MonotonicityNot monotonic
2023-03-22T15:20:05.466913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.67 9
 
0.2%
30.6 9
 
0.2%
30.11 9
 
0.2%
32.18 9
 
0.2%
27.12 9
 
0.2%
26.18 9
 
0.2%
24.43 8
 
0.2%
31.45 8
 
0.2%
28.63 8
 
0.2%
31.37 8
 
0.2%
Other values (1902) 4635
98.2%
ValueCountFrequency (%)
4.4 1
< 0.1%
4.59 1
< 0.1%
5.08 1
< 0.1%
5.25 1
< 0.1%
5.78 1
< 0.1%
5.87 1
< 0.1%
5.97 1
< 0.1%
6.36 1
< 0.1%
6.41 1
< 0.1%
6.43 1
< 0.1%
ValueCountFrequency (%)
57.53 1
< 0.1%
57.36 1
< 0.1%
57.04 1
< 0.1%
56.83 1
< 0.1%
56.59 1
< 0.1%
56.46 1
< 0.1%
56.07 1
< 0.1%
55.78 1
< 0.1%
55.51 1
< 0.1%
55.47 1
< 0.1%

totaleveminutes
Real number (ℝ)

Distinct1833
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.72156
Minimum52.9
Maximum349.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:05.620284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum52.9
5-th percentile118.9
Q1166.7
median201.1
Q3233.9
95-th percentile283.3
Maximum349.4
Range296.5
Interquartile range (IQR)67.2

Descriptive statistics

Standard deviation49.661687
Coefficient of variation (CV)0.2474158
Kurtosis-0.14692218
Mean200.72156
Median Absolute Deviation (MAD)33.9
Skewness0.020262572
Sum947606.5
Variance2466.2831
MonotonicityNot monotonic
2023-03-22T15:20:05.745283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169.9 10
 
0.2%
199.7 10
 
0.2%
230.9 10
 
0.2%
194 9
 
0.2%
167.6 9
 
0.2%
187.5 9
 
0.2%
230 8
 
0.2%
195.5 8
 
0.2%
196 8
 
0.2%
209.9 8
 
0.2%
Other values (1823) 4632
98.1%
ValueCountFrequency (%)
52.9 1
< 0.1%
53.2 1
< 0.1%
56 1
< 0.1%
58.6 1
< 0.1%
58.9 1
< 0.1%
60 1
< 0.1%
60.8 1
< 0.1%
61.9 1
< 0.1%
64.3 1
< 0.1%
65.2 1
< 0.1%
ValueCountFrequency (%)
349.4 1
< 0.1%
348.9 1
< 0.1%
348.5 1
< 0.1%
347.3 1
< 0.1%
345.1 1
< 0.1%
344.9 1
< 0.1%
344 1
< 0.1%
341.3 1
< 0.1%
340.3 1
< 0.1%
339.9 1
< 0.1%

totalevecalls
Real number (ℝ)

Distinct117
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.25842
Minimum42
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:05.886383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile68
Q187
median101
Q3114
95-th percentile133
Maximum159
Range117
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.578075
Coefficient of variation (CV)0.19527611
Kurtosis-0.12345086
Mean100.25842
Median Absolute Deviation (MAD)13
Skewness0.0047917731
Sum473320
Variance383.301
MonotonicityNot monotonic
2023-03-22T15:20:06.028087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 111
 
2.4%
97 105
 
2.2%
91 104
 
2.2%
103 101
 
2.1%
94 98
 
2.1%
101 97
 
2.1%
104 96
 
2.0%
96 95
 
2.0%
109 94
 
2.0%
111 93
 
2.0%
Other values (107) 3727
78.9%
ValueCountFrequency (%)
42 1
 
< 0.1%
43 1
 
< 0.1%
44 2
 
< 0.1%
45 1
 
< 0.1%
46 5
0.1%
47 2
 
< 0.1%
48 6
0.1%
49 2
 
< 0.1%
50 4
0.1%
51 6
0.1%
ValueCountFrequency (%)
159 1
 
< 0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
155 5
0.1%
154 4
0.1%
153 1
 
< 0.1%
152 7
0.1%
151 4
0.1%
150 6
0.1%
149 8
0.2%

totalevecharge
Real number (ℝ)

Distinct1619
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.061561
Minimum4.5
Maximum29.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:06.177793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile10.11
Q114.17
median17.09
Q319.88
95-th percentile24.08
Maximum29.7
Range25.2
Interquartile range (IQR)5.71

Descriptive statistics

Standard deviation4.2212269
Coefficient of variation (CV)0.24741153
Kurtosis-0.14702755
Mean17.061561
Median Absolute Deviation (MAD)2.88
Skewness0.020300672
Sum80547.63
Variance17.818757
MonotonicityNot monotonic
2023-03-22T15:20:06.325790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.12 14
 
0.3%
14.25 14
 
0.3%
16.97 13
 
0.3%
18.96 13
 
0.3%
15.9 12
 
0.3%
18.79 12
 
0.3%
19.41 12
 
0.3%
16.41 11
 
0.2%
16.18 11
 
0.2%
18.62 11
 
0.2%
Other values (1609) 4598
97.4%
ValueCountFrequency (%)
4.5 1
< 0.1%
4.52 1
< 0.1%
4.76 1
< 0.1%
4.98 1
< 0.1%
5.01 1
< 0.1%
5.1 1
< 0.1%
5.17 1
< 0.1%
5.26 1
< 0.1%
5.47 1
< 0.1%
5.54 1
< 0.1%
ValueCountFrequency (%)
29.7 1
< 0.1%
29.66 1
< 0.1%
29.62 1
< 0.1%
29.52 1
< 0.1%
29.33 1
< 0.1%
29.32 1
< 0.1%
29.24 1
< 0.1%
29.01 1
< 0.1%
28.93 1
< 0.1%
28.89 1
< 0.1%

totalnightminutes
Real number (ℝ)

Distinct1809
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.39343
Minimum53.3
Maximum349.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:06.485839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum53.3
5-th percentile117.9
Q1167.1
median200.4
Q3234.7
95-th percentile282.5
Maximum349.2
Range295.9
Interquartile range (IQR)67.6

Descriptive statistics

Standard deviation49.585817
Coefficient of variation (CV)0.24744233
Kurtosis-0.19070447
Mean200.39343
Median Absolute Deviation (MAD)33.7
Skewness-0.013386375
Sum946057.4
Variance2458.7533
MonotonicityNot monotonic
2023-03-22T15:20:06.626792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188.2 11
 
0.2%
194.3 11
 
0.2%
208.9 10
 
0.2%
186.2 10
 
0.2%
228.1 10
 
0.2%
191.4 9
 
0.2%
214.7 9
 
0.2%
192.7 9
 
0.2%
197.4 9
 
0.2%
193.6 9
 
0.2%
Other values (1799) 4624
97.9%
ValueCountFrequency (%)
53.3 1
< 0.1%
54 1
< 0.1%
54.5 1
< 0.1%
56.6 1
< 0.1%
57.5 1
< 0.1%
59.5 1
< 0.1%
60.3 1
< 0.1%
61.4 1
< 0.1%
63.3 1
< 0.1%
63.6 1
< 0.1%
ValueCountFrequency (%)
349.2 1
< 0.1%
345.8 1
< 0.1%
344.3 1
< 0.1%
342.8 1
< 0.1%
336.1 1
< 0.1%
334.7 1
< 0.1%
333.5 2
< 0.1%
332.8 1
< 0.1%
332.7 1
< 0.1%
332.2 2
< 0.1%

totalnightcalls
Real number (ℝ)

Distinct116
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.905317
Minimum42
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:06.770792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile67
Q187
median100
Q3113
95-th percentile132
Maximum159
Range117
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.596451
Coefficient of variation (CV)0.19615024
Kurtosis-0.15723154
Mean99.905317
Median Absolute Deviation (MAD)13
Skewness0.0081001396
Sum471653
Variance384.02091
MonotonicityNot monotonic
2023-03-22T15:20:06.902793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 115
 
2.4%
100 101
 
2.1%
102 101
 
2.1%
91 101
 
2.1%
104 101
 
2.1%
99 100
 
2.1%
94 99
 
2.1%
98 98
 
2.1%
95 96
 
2.0%
103 95
 
2.0%
Other values (106) 3714
78.7%
ValueCountFrequency (%)
42 4
 
0.1%
43 1
 
< 0.1%
44 1
 
< 0.1%
46 3
 
0.1%
48 3
 
0.1%
49 4
 
0.1%
50 5
0.1%
51 4
 
0.1%
52 4
 
0.1%
53 10
0.2%
ValueCountFrequency (%)
159 2
 
< 0.1%
158 2
 
< 0.1%
157 2
 
< 0.1%
156 1
 
< 0.1%
155 4
0.1%
154 3
 
0.1%
153 3
 
0.1%
152 2
 
< 0.1%
151 8
0.2%
150 5
0.1%

totalnightcharge
Real number (ℝ)

Distinct999
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0178183
Minimum2.4
Maximum15.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:07.044576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.4
5-th percentile5.31
Q17.52
median9.02
Q310.56
95-th percentile12.71
Maximum15.71
Range13.31
Interquartile range (IQR)3.04

Descriptive statistics

Standard deviation2.2313705
Coefficient of variation (CV)0.24744018
Kurtosis-0.19080617
Mean9.0178183
Median Absolute Deviation (MAD)1.52
Skewness-0.013421393
Sum42573.12
Variance4.9790145
MonotonicityNot monotonic
2023-03-22T15:20:07.192103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.47 19
 
0.4%
9.66 18
 
0.4%
8.15 18
 
0.4%
10.26 18
 
0.4%
10.8 17
 
0.4%
10.49 17
 
0.4%
9.63 17
 
0.4%
9.4 17
 
0.4%
9.76 16
 
0.3%
9.65 16
 
0.3%
Other values (989) 4548
96.3%
ValueCountFrequency (%)
2.4 1
< 0.1%
2.43 1
< 0.1%
2.45 1
< 0.1%
2.55 1
< 0.1%
2.59 1
< 0.1%
2.68 1
< 0.1%
2.71 1
< 0.1%
2.76 1
< 0.1%
2.85 1
< 0.1%
2.86 1
< 0.1%
ValueCountFrequency (%)
15.71 1
< 0.1%
15.56 1
< 0.1%
15.49 1
< 0.1%
15.43 1
< 0.1%
15.12 1
< 0.1%
15.06 1
< 0.1%
15.01 2
< 0.1%
14.98 1
< 0.1%
14.97 1
< 0.1%
14.95 2
< 0.1%

totalintlminutes
Real number (ℝ)

Distinct153
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.319996
Minimum2.5
Maximum18.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:07.333100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile5.9
Q18.6
median10.4
Q312
95-th percentile14.6
Maximum18.2
Range15.7
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation2.6119945
Coefficient of variation (CV)0.25310035
Kurtosis-0.12421981
Mean10.319996
Median Absolute Deviation (MAD)1.7
Skewness-0.015032267
Sum48720.7
Variance6.8225153
MonotonicityNot monotonic
2023-03-22T15:20:07.471102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.8 85
 
1.8%
11.1 84
 
1.8%
10.5 78
 
1.7%
11.3 78
 
1.7%
10.1 78
 
1.7%
11.4 78
 
1.7%
10.9 77
 
1.6%
10 74
 
1.6%
10.6 74
 
1.6%
11 74
 
1.6%
Other values (143) 3941
83.5%
ValueCountFrequency (%)
2.5 1
 
< 0.1%
2.6 1
 
< 0.1%
2.7 1
 
< 0.1%
2.9 2
 
< 0.1%
3.1 2
 
< 0.1%
3.3 2
 
< 0.1%
3.4 2
 
< 0.1%
3.5 5
0.1%
3.6 2
 
< 0.1%
3.7 5
0.1%
ValueCountFrequency (%)
18.2 2
< 0.1%
18 3
0.1%
17.9 1
 
< 0.1%
17.8 3
0.1%
17.7 1
 
< 0.1%
17.6 2
< 0.1%
17.5 3
0.1%
17.3 3
0.1%
17.2 3
0.1%
17.1 1
 
< 0.1%

totalintlcalls
Real number (ℝ)

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3226011
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:07.603135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum11
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1522396
Coefficient of variation (CV)0.49790381
Kurtosis0.30889288
Mean4.3226011
Median Absolute Deviation (MAD)1
Skewness0.7903479
Sum20407
Variance4.6321353
MonotonicityNot monotonic
2023-03-22T15:20:07.689132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 961
20.4%
4 912
19.3%
2 714
15.1%
5 684
14.5%
6 477
10.1%
7 295
 
6.2%
1 249
 
5.3%
8 169
 
3.6%
9 143
 
3.0%
10 74
 
1.6%
ValueCountFrequency (%)
1 249
 
5.3%
2 714
15.1%
3 961
20.4%
4 912
19.3%
5 684
14.5%
6 477
10.1%
7 295
 
6.2%
8 169
 
3.6%
9 143
 
3.0%
10 74
 
1.6%
ValueCountFrequency (%)
11 43
 
0.9%
10 74
 
1.6%
9 143
 
3.0%
8 169
 
3.6%
7 295
 
6.2%
6 477
10.1%
5 684
14.5%
4 912
19.3%
3 961
20.4%
2 714
15.1%

totalintlcharge
Real number (ℝ)

Distinct153
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7869159
Minimum0.68
Maximum4.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:07.804103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.68
5-th percentile1.59
Q12.32
median2.81
Q33.24
95-th percentile3.94
Maximum4.91
Range4.23
Interquartile range (IQR)0.92

Descriptive statistics

Standard deviation0.70516393
Coefficient of variation (CV)0.25302663
Kurtosis-0.12414773
Mean2.7869159
Median Absolute Deviation (MAD)0.46
Skewness-0.015054879
Sum13157.03
Variance0.49725616
MonotonicityNot monotonic
2023-03-22T15:20:07.935107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.65 85
 
1.8%
3 84
 
1.8%
2.84 78
 
1.7%
3.05 78
 
1.7%
2.73 78
 
1.7%
3.08 78
 
1.7%
2.94 77
 
1.6%
2.7 74
 
1.6%
2.86 74
 
1.6%
2.97 74
 
1.6%
Other values (143) 3941
83.5%
ValueCountFrequency (%)
0.68 1
 
< 0.1%
0.7 1
 
< 0.1%
0.73 1
 
< 0.1%
0.78 2
 
< 0.1%
0.84 2
 
< 0.1%
0.89 2
 
< 0.1%
0.92 2
 
< 0.1%
0.95 5
0.1%
0.97 2
 
< 0.1%
1 5
0.1%
ValueCountFrequency (%)
4.91 2
< 0.1%
4.86 3
0.1%
4.83 1
 
< 0.1%
4.81 3
0.1%
4.78 1
 
< 0.1%
4.75 2
< 0.1%
4.73 3
0.1%
4.67 3
0.1%
4.64 3
0.1%
4.62 1
 
< 0.1%
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5255243
Minimum0
Maximum5
Zeros971
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size73.8 KiB
2023-03-22T15:20:08.039147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2119789
Coefficient of variation (CV)0.79446715
Kurtosis0.017259047
Mean1.5255243
Median Absolute Deviation (MAD)1
Skewness0.70614853
Sum7202
Variance1.4688929
MonotonicityNot monotonic
2023-03-22T15:20:08.130136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 1691
35.8%
2 1090
23.1%
0 971
20.6%
3 635
 
13.5%
4 244
 
5.2%
5 90
 
1.9%
ValueCountFrequency (%)
0 971
20.6%
1 1691
35.8%
2 1090
23.1%
3 635
 
13.5%
4 244
 
5.2%
5 90
 
1.9%
ValueCountFrequency (%)
5 90
 
1.9%
4 244
 
5.2%
3 635
 
13.5%
2 1090
23.1%
1 1691
35.8%
0 971
20.6%

Interactions

2023-03-22T15:20:00.498098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:23.729732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:26.092760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:28.730165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:31.156775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:33.679099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:36.624320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:39.344175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:41.930149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:44.967932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:47.676146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:50.322557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:53.081834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:56.499452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:58.557384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:00.616733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:23.883127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:26.229401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:28.876480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:31.301776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:33.873255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:36.804572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:39.522180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:42.092157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:45.132560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:47.839007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:50.489147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:53.233842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:56.632448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:58.704377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:00.757729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:24.031261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:26.418408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:29.033446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:31.463223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:34.047259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:36.986472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:39.702753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:42.245862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:45.302599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:47.984005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:50.672120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:53.434440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:56.753450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:58.832097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:00.906723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:24.166300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:26.634772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:29.195947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:31.629188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:34.239377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:37.195192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:39.883742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:42.499259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:45.521627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:48.131637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:50.876119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:53.635493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:56.885453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:58.950097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:01.032798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:24.379060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:26.836871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:29.365140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:31.797188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:34.420981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:37.382608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:40.045745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:42.820228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:45.737617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:48.311646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:51.044111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:53.833153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:57.076080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:59.068097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:01.157655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:24.560185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:26.992101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:29.511988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:31.927562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:34.586429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:37.540901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:40.203392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:43.045249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:45.940254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:48.528121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:51.190121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:54.035147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:57.231737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:59.183097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:01.314671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:24.729305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:27.124936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:29.653034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:32.104206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:34.793314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:37.736702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:40.356451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:43.203245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:46.102252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:48.735144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:51.349150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:54.295167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:57.375607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:59.308095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:01.462659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:24.886437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:27.274425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:29.842648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:32.251316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:34.965274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:37.894678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:40.534452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:43.379357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:46.274251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:48.930123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:51.494262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:54.513152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:57.511605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:59.435139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:01.582674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:25.017032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:27.461104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:29.982906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:32.444693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:35.325522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:38.085719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:40.719527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:43.558347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:46.443238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:49.060124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:51.651264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:54.745890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:57.653605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:59.557618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:01.705281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:25.165948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:27.642852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:30.125725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:32.632202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:35.546228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:38.280124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:40.894517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:43.743023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:46.633249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:49.226518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:51.815197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:55.252532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:57.773708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:59.679623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:01.832295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:25.330291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:27.828863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:30.270684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:32.821408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:35.771256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:38.475655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:41.049425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:43.939080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:46.843238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:49.436521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:51.978212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:55.537544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:57.894712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:59.805622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:01.975284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:25.514902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:27.988901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:30.485718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:32.994309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:35.952116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:38.673644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:41.212425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:44.295079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:47.029150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:49.669518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:52.157209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:55.770555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:58.034709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:59.928624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:02.131978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:25.710886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:28.154448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:30.671818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:33.145980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:36.129341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:38.854642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:41.402089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:44.472085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:47.202156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:49.830518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:52.356208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:56.016163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:58.153709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:00.061610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:02.270977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:25.831006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:28.297354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:30.836735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:33.311210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:36.294122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:39.010714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:41.539090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:44.626066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:47.363155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:50.000511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:52.570246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:56.173814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:58.272727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:00.211103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:02.421980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:25.965886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:28.563632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:30.995115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:33.465734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:36.445745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:39.167186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:41.735158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:44.791945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:47.518149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:50.161509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:52.826839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:56.326454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:19:58.393371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-22T15:20:00.359116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-03-22T15:20:08.244762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
accountlengthnumbervmailmessagestotaldayminutestotaldaycallstotaldaychargetotaleveminutestotalevecallstotalevechargetotalnightminutestotalnightcallstotalnightchargetotalintlminutestotalintlcallstotalintlchargenumbercustomerservicecallschurninternationalplanvoicemailplan
accountlength1.000-0.0070.0050.0260.005-0.0180.007-0.018-0.003-0.005-0.0030.0120.0190.012-0.0080.0000.0000.021
numbervmailmessages-0.0071.0000.010-0.0020.0100.024-0.0040.0240.001-0.0050.0010.002-0.0200.002-0.0140.1100.0060.998
totaldayminutes0.0050.0101.0000.0061.000-0.0080.015-0.0080.005-0.0020.005-0.027-0.009-0.0270.0020.3630.0460.014
totaldaycalls0.026-0.0020.0061.0000.0060.0100.0090.0100.002-0.0050.0020.0040.0130.004-0.0140.0170.0000.022
totaldaycharge0.0050.0101.0000.0061.000-0.0080.015-0.0080.005-0.0020.005-0.027-0.009-0.0270.0020.3630.0450.011
totaleveminutes-0.0180.024-0.0080.010-0.0081.0000.0011.000-0.0210.011-0.0210.0130.0100.013-0.0230.0890.0200.033
totalevecalls0.007-0.0040.0150.0090.0150.0011.0000.0010.016-0.0180.016-0.0120.004-0.0120.0050.0330.0000.024
totalevecharge-0.0180.024-0.0080.010-0.0081.0000.0011.000-0.0210.011-0.0210.0130.0100.013-0.0230.0880.0190.033
totalnightminutes-0.0030.0010.0050.0020.005-0.0210.016-0.0211.0000.0141.000-0.006-0.010-0.006-0.0180.0390.0310.006
totalnightcalls-0.005-0.005-0.002-0.005-0.0020.011-0.0180.0110.0141.0000.0140.0100.0030.0100.0010.0000.0000.000
totalnightcharge-0.0030.0010.0050.0020.005-0.0210.016-0.0211.0000.0141.000-0.006-0.010-0.006-0.0180.0410.0340.000
totalintlminutes0.0120.002-0.0270.004-0.0270.013-0.0120.013-0.0060.010-0.0061.000-0.0071.000-0.0100.1020.0330.000
totalintlcalls0.019-0.020-0.0090.013-0.0090.0100.0040.010-0.0100.003-0.010-0.0071.000-0.0070.0030.0820.0000.000
totalintlcharge0.0120.002-0.0270.004-0.0270.013-0.0120.013-0.0060.010-0.0061.000-0.0071.000-0.0100.1020.0330.000
numbercustomerservicecalls-0.008-0.0140.002-0.0140.002-0.0230.005-0.023-0.0180.001-0.018-0.0100.003-0.0101.0000.2850.0000.027
churn0.0000.1100.3630.0170.3630.0890.0330.0880.0390.0000.0410.1020.0820.1020.2851.0000.2550.111
internationalplan0.0000.0060.0460.0000.0450.0200.0000.0190.0310.0000.0340.0330.0000.0330.0000.2551.0000.000
voicemailplan0.0210.9980.0140.0220.0110.0330.0240.0330.0060.0000.0000.0000.0000.0000.0270.1110.0001.000

Missing values

2023-03-22T15:20:02.643439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-22T15:20:02.926488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

churnaccountlengthinternationalplanvoicemailplannumbervmailmessagestotaldayminutestotaldaycallstotaldaychargetotaleveminutestotalevecallstotalevechargetotalnightminutestotalnightcallstotalnightchargetotalintlminutestotalintlcallstotalintlchargenumbercustomerservicecalls
001280125265.111045.07197.49916.78244.79111.0110.032.701
101070126161.612327.47195.510316.62254.410311.4513.733.701
20137000243.411441.38121.211010.30162.61047.3212.253.290
3084100299.47150.9061.9885.26196.9898.866.671.782
4075100166.711328.34148.312212.61186.91218.4110.132.733
50118100223.49837.98220.610118.75203.91189.186.361.700
601210124218.28837.09348.510829.62212.61189.577.572.033
70147100157.07926.69103.1948.76211.8969.537.161.920
901411137258.68443.96222.011118.87326.49714.6911.253.020
10165000129.113721.95228.58319.42208.81119.4012.763.434
churnaccountlengthinternationalplanvoicemailplannumbervmailmessagestotaldayminutestotaldaycallstotaldaychargetotaleveminutestotalevecallstotalevechargetotalnightminutestotalnightcallstotalnightchargetotalintlminutestotalintlcallstotalintlchargenumbercustomerservicecalls
49890150000170.011528.90162.713813.83267.27712.028.322.240
49901140000244.711541.60258.610121.98231.311210.417.562.031
4991197000252.68942.94340.39128.93256.56711.548.852.381
4992083000188.37032.01243.88820.72213.7799.6210.362.780
4993073000177.98930.24131.28211.15186.2898.3811.563.113
4994075000170.710129.02193.112616.41129.11045.816.971.861
49950500140235.712740.07223.012618.96297.511613.399.952.672
49961152000184.29031.31256.87321.83213.61139.6114.723.973
4997061000140.68923.90172.812814.69212.4979.5613.643.671
49980109000188.86732.10171.79214.59224.48910.108.562.300